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24th International Conference on Control systems and computer Science, CSCS 2023

作者： Voicu, George Catalin Ciolofan, Sorin N. Dragoicea, Monica Manager Emag Bucharest Romania Politehnica University of Bucharest Department of Computer Science Bucharest Romania Politehnica University of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

It is well known that nowadays children tend to spend lot of time playing games on mobile devices at the expense of reading books. The current research aims to explore ways to design and develop a mobile application, attractive for children, that uses voice recognition technology and gamification techniques to help children read more and better and to track the progress of reading. The application is implemented with Flutter, Dart and Firebase and is addressed to children, the books being made available in encyclopedia format. By integrating technologies such as NLP and gamification the application aims to improve reading fluency among children, but also to give them the opportunity to accumulate new and exciting information in the same time. © 2023 IEEE.

关键词： Human computer interaction

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Unsupervised High Impedance Fault Detection Using Autoencoder and Principal Component Analysis

Unsupervised High Impedance Fault Detection Using Autoencode...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Liu, Yingxiang Razeghi-Jahromi, Mohammad Stoupis, James University of Southern California Department of Electrical and Computer Engineering Los Angeles United States Abb Us Research Center Principal Scientist at Electrical Systems Department Raleigh United States Abb Us Research Center Electrical Systems Department Manager Raleigh United States

ISBN: (纸本)9798350320169

Detection of high impedance faults (HIFs) in power distribution networks remains a challenging task. Due to the low current magnitude and diverse characteristics of HIFs, conventional over-current relays are ineffective in detecting such faults. Machine learning-based methods have emerged as a promising solution, but most rely on supervised learning techniques to detect HIF by performing classifications that require a significant amount of HIF data that are difficult to acquire in the real world. As a result, the reliability and generalization of these models are limited when the load profiles and faults are not present in the training data. In this paper, we propose an unsupervised HIF detection framework that employs autoencoder and principal component analysis-based monitoring techniques to identify changes in the correlation structure within the current waveforms that deviate from normal loads. The performance of the proposed HIF detection method is tested using real data collected from a 4.16 kV distribution system and compared with results from a commercially available solution for HIF detection. The numerical results demonstrate the superiority of our proposed method over the commercially available HIF detection technique while ensuring high security by avoiding false detections during normal load conditions. © 2023 IEEE.

关键词： Principal component analysis

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Bullyproof Sentinel: Fortifying Online Spaces Against Bullying using Machine Learning 7

Bullyproof Sentinel: Fortifying Online Spaces Against Bullyi...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Dilshani, M.M.T. Vinoshan, T. Wijesinghe, W.A.S.D. Jathushan, V. Senarathne, Amila Gunasinghe, Amali Sri Lanka Institute of Information Technology Faculty of Computing Cyber Security Specialization Malabe Sri Lanka Sliit Cisco Academy Sri Lanka Institute of Information Technology Industry Engagement Unit Department of Computer Systems Engineering Manager Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341485

In the current social media landscape, cyberbullying and online harassment remain pressing issues. Therefore, we planned a cyberbullying detection system for social media platforms. It can protect online communities from toxic behaviors and make a global impact by fostering a safer and more constructive online environment. We are mainly focused on developing an improved technique to detect bot profiles on social media, developing a mechanism to detect abusive content and sarcasm, developing a mechanism to detect defamatory comments on social media, and developing an effective method for detecting offensive and abusive content in image-format reviews on social media using machine learning algorithms. © 2023 IEEE.

关键词： Machine learning

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Making children read more and better by integrating natural language processing and gamification techniques into mobile applications

Making children read more and better by integrating natural ...

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International Conference on Control systems and computer Science (CSCS)

作者： George Cătălin Voicu Sorin N. Ciolofan Monica Drăgoicea Manager eMAG Bucharest Romania Department of Computer Science Politehnica University of Bucharest Bucharest Romania Department of Automatic Control and Systems Engineering Politehnica University of Bucharest Bucharest Romania

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Unsupervised High Impedance Fault Detection Using Autoencoder and Principal Component Analysis

Unsupervised High Impedance Fault Detection Using Autoencode...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Yingxiang Liu Mohammad Razeghi-Jahromi James Stoupis Department of Electrical and Computer Engineering University of Southern California Los Angeles USA Principal Scientist at Electrical Systems Department ABB US Research Center Raleigh USA Electrical Systems Department Manager ABB US Research Center Raleigh USA

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A Deep Learning Framework for Object Detection and Tracking: CNN-Based Parcel Tracking on Conveyor Belts

A Deep Learning Framework for Object Detection and Tracking:...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md Jobair Hossain Faruk Md Kamrul Siam Alex Theilmann Kristiyan Georgiev Jerry Q. Cheng Hossain Shahriar Department of Computer Science New York Institute of Technology USA Nova School of Business and Economics Portugal R&D Computer Vision and Software Engineering Manager Materials Handling Systems Inc. USA Center for Cybersecurity University of West Florida USA

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Object Detection and Tracking (ODT) represents a critical area within computer vision, with broad applications across various industries, particularly in the automation of logistics operations. However, significant challenges persist, including the logistics-specific datasets and the inefficiency of data annotation, which is crucial for training robust artificial intelligence models for parcel detection and tracking. These challenges are exacerbated when human experts are required to manually assign unique identifiers to objects across frames, hindering the scalability of AI model training. In this research, we present a novel deep learning-based ODT framework, leveraging Convolutional Neural Networks (CNN) integrated with evaluation metrics such as Euclidean Distance and Intersection over Union (IoU) to facilitate robust object detection and tracking. To address the limitations of manual annotation, we develop an automated framework for assigning unique identifiers to bounding boxes across sequential frames. The framework is based on three logistics-related datasets, comprising time-series images of packages on moving conveyor belts. The first two datasets, containing 100 and 1,021 images, are manually annotated, while the third dataset of 5,046 images is automatically annotated using the proposed tool. We also utilize the Hungarian algorithm to optimize identity assignment across frames. The results demonstrate that the framework achieves an accuracy of 99.09% in object detection and tracking with a precision of 99.25% and a recall of 99.10% for the manually annotated dataset. The automated annotation illustrates an accuracy of 98.70%. These findings indicate the robustness and scalability of the proposed framework in automating parcel tracking for logistics operations.

关键词： Training computer vision Accuracy Annotations Scalability Object detection Robustness Convolutional neural networks Object tracking Logistics

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Bullyproof Sentinel: Fortifying Online Spaces Against Bullying using Machine Learning

Bullyproof Sentinel: Fortifying Online Spaces Against Bullyi...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Dilshani M.M.T. Vinoshan T. Wijesinghe W.A.S.D. Jathushan V. Amila Senarathne Amali Gunasinghe Faculty of Computing Cyber Security Specialization Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Manager Industry Engagement Unit SLIIT CISCO Academy Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

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IoTFLiP： IoT-based flipped learning platform for medical education

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Digital Communications and Networks 2017年第3期3卷 188-194页

作者： Maqbool Ali Hafiz Syed Muhammad Bilal Muhammad Asif Razzaq Jawad Khan Sungyoung Lee Muhammad Idris Mohammad Aazam Taebong Choi Soyeon Caren Han Byeong Ho Kang Department of Computer Science and Engineering Kyung Hee University Yongin 446-701 Republic of Korea IT4BI Universite Libre de Bruxelles Belgium Carleton University Canada Hanwha Systems as Senior Manager Communication PM Team Seongnam 13524 Republic of Korea School of Engineering and ICT Uniuersity of Tasmania Hobart Tasmania 7005 Australia

Case-Based Learning （CBL） has become an effective pedagogy for student-centered learning in medical education, which is founded on persistent patient cases. Flippped learning and Internet of Things （IoTs） concepts have gained significant attention in recent years. Using these concepts in conjunction with CBL can improve learning ability by providing real evolutionary medical eases. It also enables students to build confidence in their decision making, and efficiently enhances teamwork in the learning environment. We propose an IoT-based Flip Learning Platform, called IoTFLiP, where an IoT infrastrneture is exploited to support flipped case-based learning in a cloud environment with state of the art security and privacy measures for personalized medical data. It also provides support for application delivery in private, public, and hybrid approaches. The proposed platform is an extension of our Interactive Case-Based Flipped Learning Tool （ICBFLT）, which has been developed based on current CBL practices. ICBFLT formulates summaries of CBL cases through synergy between students＇ and medical expert knowledge. The low cost and reduced size of sensor device, support of IoTs, and recent flipped learning advancements can enhance medical students＇ academic and practical experiences. In order to demonstrate a working scenario for the proposed IoTFLiP platform, real-time data from IoTs gadgets is collected to generate a real-world case for a medical student using ICBFLT.

关键词： Internet of things Cloud environment Flipped learning Case-based learning Medical education

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Fault Tolerant Margins of an UAV at Uncertain Environment

Fault Tolerant Margins of an UAV at Uncertain Environment

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International Conference on Unmanned Aircraft systems

作者： Chimpalthradi R. Ashokkumar George WP York Scott F. Gruber the Department of Electrical and Computer Engineering US Air Force Academy Colorado Springs CO 80840 the USAF Academy Center for Unmanned Aircraft Systems Research in the Department of Electrical and Computer Engineering.US Air Force Academy Colorado Spring CO 80840. Program Manager at the USAF Academy Center for Unmanned Aircraft Systems Research in the Department of Electrical and Computer Engineering.US Air Force Academy Colorado Spring CO 80840.

ISBN: (纸本)9781467393355

In fault tolerant control, the damaged and healthy states of a sensor output or a control input are captured by using an uncertain parameter. For instance, the control input u(t) resulting from an actuator is modeled σu(t) and the healthy and damaged conditions are inferred from σ which takes the values σ=1 and σ in [0, 1), respectively [1-3]. Given a stabilizing controller, a range of σ-values in the interval [0,1) for which the closed loop system would remain stable is referred as the faulttolerant margin. In this paper, several of these margins are presented when aerodynamic parameter variations due to an uncertain atmospheric condition in an unmanned aerial vehicle (UAV) are present. It is shown that the fault tolerant margins are extremely sensitive to these parameter perturbations. Some perturbation locations can significantly reduce the stable range for parameter values as well as for the fault tolerant margins. An UAV model is considered to illustrate the fault tolerant margins and admissible parameter variations. Secondly, the algorithm applied to compute the fault tolerant margins is used to solve the examples of Professor Ross B Barmish et al [29] and hence the exact stability margins for a two parameter system are illustrated. However, when the number of parameters increases, the algorithm becomes computationally intensive.

关键词： Fault Tolerant Margins

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Printing multi-functionality: Additive manufacturing for cubesats

Printing multi-functionality: Additive manufacturing for cub...

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AIAA SPACE 2014 Conference and Exposition

作者： Kief, Craig J. Aarestad, Jim MacDonald, Eric Shemelya, Corey Roberson, David Wicker, Ryan Kwas, Andy M. Zemba, Mike Avery, Keith Netzer, Richard Kemp, William W. M. Keck Center for 3D Innovation University of Texs at El Paso El Paso TX 79902 United States Northrop Grumman Technical Services Albuquerque NM 87109 United States NASA Glenn Research Center Cleveland OH 44135 United States Air Force Research Laboratory Albuquerque NM 87117 United States LEIDOS Albuquerque NM 87109 United States COSMIAC 2350 Alamo Avenue SE United States Department of Electrical and Computer Engineering United States Department of Materials Science and Engineering United States Department of Mechanical Engineering United States Dept. of Electrical and Computer Engineering UNM Engineering Manager Northrop Grumman Technical Services 100 Sun Ave United States Antena and Optics Systems Branch 21000 Brookpark Rd United States AFRL/RVSE 3550 Abdeen Ave SE Albuquerque NM 87117 United States 2109 Air Park Rd. SE Ste 150 Albuquerque NM 87106 United States

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